Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units
Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units
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DOI：
10.3390/en13153914
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发表时间：
2020-07
期刊：
影响因子：
3.2
通讯作者：
Majid Hosseini;Satya Katragadda;Jessica Wojtkiewicz;Raju N. Gottumukkala;A. Maida;T. Chambers
中科院分区：
文献类型：
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作者：
Majid Hosseini;Satya Katragadda;Jessica Wojtkiewicz;Raju N. Gottumukkala;A. Maida;T. Chambers
电网运营商依靠太阳辐照度预测来管理与太阳能相关的不确定性和可变性。气象因素，如云量、风向和风速影响辐照度，并与高度的可变性和不确定性有关。统计模型无法准确地捕捉这些因素和辐照度之间的依赖关系。本文介绍了应用多元门控回归单元（GRU）预报直接法向辐照度（DNI）逐时变化的思想。所提出的基于GRU的预测方法使用历史辐照度数据（即，天气变量（包括云量、风向和风速）来预测小时内和小时间的辐照度预测。我们对测量和仪器数据中心的一个站点的评估表明，在不同条件下评估时，GRU和LSTM都提高了DNI预测性能。此外，包括风向和风速可以大大提高DNI预报的准确性。此外，预测模型可以准确地预测多个预测期的辐照度值。
Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with solar power. Meteorological factors such as cloud cover, wind direction, and wind speed affect irradiance and are associated with a high degree of variability and uncertainty. Statistical models fail to accurately capture the dependence between these factors and irradiance. In this paper, we introduce the idea of applying multivariate Gated Recurrent Units (GRU) to forecast Direct Normal Irradiance (DNI) hourly. The proposed GRU-based forecasting method is evaluated against traditional Long Short-Term Memory (LSTM) using historical irradiance data (i.e., weather variables that include cloud cover, wind direction, and wind speed) to forecast irradiance forecasting over intra-hour and inter-hour intervals. Our evaluation on one of the sites from Measurement and Instrumentation Data Center indicate that both GRU and LSTM improved DNI forecasting performance when evaluated under different conditions. Moreover, including wind direction and wind speed can have substantial improvement in the accuracy of DNI forecasts. Besides, the forecasting model can accurately forecast irradiance values over multiple forecasting horizons.